Automated data collection and analytics for the RoyalRoad platform
This project was aimed at creating a high-performance server solution for automated data collection and analysis from the literary platform RoyalRoad. The main task was to gather detailed information about 37,000 books, convert it into a structured format, and provide the capability for in-depth analysis for further use.
Functional capabilities of the application:
Data collection:
Extraction of information about books, including titles, ratings, descriptions, categories, and key tags.
Collection of information about authors: names, profile links, activity on the platform.
Obtaining detailed information about chapters, including the number of chapters, publication dates, and their popularity.
Data structuring:
Automated conversion of the collected information into JSON format, with a clear structure for convenient use.
Storing data in a database to optimize access and further analysis.
Data analysis and report generation:
Sorting and filtering books by rating, number of chapters, popularity among readers.
Development of algorithms to identify trends, such as the most popular genres or authors.
Preparation of reports with specific analytical insights for use in business purposes or research.
Main challenges and their solutions:
Bypassing anti-scraping protection:
A bypass of the platform's protection systems, such as CAPTCHA and request limits, was implemented. This ensured a stable and continuous data collection process.
Processing large volumes of data:
Optimized algorithms and efficient databases were used to ensure fast processing and storage of data.
Flexibility of the application:
The created application easily adapts to changes in the website's structure, allowing it to maintain its relevance in the long term.
Project results:
A complete dataset on 37,000 books, authors, and chapters was collected.
Tools for analytics were implemented, allowing books to be ranked by popularity, number of chapters, and ratings.
A flexible database was created, enabling effective storage and use of data for further purposes, such as recommendation systems, marketing analysis, or integration into other projects.
Functional capabilities of the application:
Data collection:
Extraction of information about books, including titles, ratings, descriptions, categories, and key tags.
Collection of information about authors: names, profile links, activity on the platform.
Obtaining detailed information about chapters, including the number of chapters, publication dates, and their popularity.
Data structuring:
Automated conversion of the collected information into JSON format, with a clear structure for convenient use.
Storing data in a database to optimize access and further analysis.
Data analysis and report generation:
Sorting and filtering books by rating, number of chapters, popularity among readers.
Development of algorithms to identify trends, such as the most popular genres or authors.
Preparation of reports with specific analytical insights for use in business purposes or research.
Main challenges and their solutions:
Bypassing anti-scraping protection:
A bypass of the platform's protection systems, such as CAPTCHA and request limits, was implemented. This ensured a stable and continuous data collection process.
Processing large volumes of data:
Optimized algorithms and efficient databases were used to ensure fast processing and storage of data.
Flexibility of the application:
The created application easily adapts to changes in the website's structure, allowing it to maintain its relevance in the long term.
Project results:
A complete dataset on 37,000 books, authors, and chapters was collected.
Tools for analytics were implemented, allowing books to be ranked by popularity, number of chapters, and ratings.
A flexible database was created, enabling effective storage and use of data for further purposes, such as recommendation systems, marketing analysis, or integration into other projects.
Lutsk 
Available for hire